Massive Open Online Courses (“MOOCs”) are quickly becoming a major factor in the world of education. The ability to log into a class from one's own personal computer or mobile device enables individuals to participate in the learning process regardless of their location. The expansion of MOOCs has also been aided, in large part, to the advancements in mobile devices, such as laptop computers, tablets, and smartphones, which are rapidly becoming more and more powerful and robust with each passing day.
However, a significant drawback to large MOOCs, or other large interactive online events, is the difficulty a teacher or presenter may encounter trying to effectively transmit the information being presented to the students/participants. In a small classroom, for example, a teacher is capable of seeing each student's face/body to gauge that student's or students' attentiveness. A confused facial expression on multiple students could mean that the subject matter, topic, or delivery technique, may not be working effectively and the teacher may augment or modify their approach accordingly. In a large online classroom, however, the ability to even see each student's face may not be possible (it may not be possible even in a small online “classroom” as well).
Thus, it would be beneficial for there to be systems and methods that allow a teacher or presenter for a massive online event, such as a MOOC, to accurately gauge a level of attentiveness, interest, and/or comprehension of the participants to aid in effectively delivering the intended message.